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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis</span></code>.LinearDiscriminantAnalysis</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-discriminant-analysis-lineardiscriminantanalysis">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis.LinearDiscriminantAnalysis</span></code></a></li>
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  <div class="section" id="sklearn-discriminant-analysis-lineardiscriminantanalysis">
<h1><a class="reference internal" href="../classes.html#module-sklearn.discriminant_analysis" title="sklearn.discriminant_analysis"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis</span></code></a>.LinearDiscriminantAnalysis<a class="headerlink" href="#sklearn-discriminant-analysis-lineardiscriminantanalysis" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.discriminant_analysis.</code><code class="sig-name descname">LinearDiscriminantAnalysis</code><span class="sig-paren">(</span><em class="sig-param">solver='svd'</em>, <em class="sig-param">shrinkage=None</em>, <em class="sig-param">priors=None</em>, <em class="sig-param">n_components=None</em>, <em class="sig-param">store_covariance=False</em>, <em class="sig-param">tol=0.0001</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/discriminant_analysis.py#L129"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="Permalink to this definition">¶</a></dt>
<dd><p>Linear Discriminant Analysis</p>
<p>A classifier with a linear decision boundary, generated by fitting class
conditional densities to the data and using Bayes’ rule.</p>
<p>The model fits a Gaussian density to each class, assuming that all classes
share the same covariance matrix.</p>
<p>The fitted model can also be used to reduce the dimensionality of the input
by projecting it to the most discriminative directions.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.17: </span><em>LinearDiscriminantAnalysis</em>.</p>
</div>
<p>Read more in the <a class="reference internal" href="../lda_qda.html#lda-qda"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>solver</strong><span class="classifier">string, optional</span></dt><dd><dl class="simple">
<dt>Solver to use, possible values:</dt><dd><ul class="simple">
<li><p>‘svd’: Singular value decomposition (default).
Does not compute the covariance matrix, therefore this solver is
recommended for data with a large number of features.</p></li>
<li><p>‘lsqr’: Least squares solution, can be combined with shrinkage.</p></li>
<li><p>‘eigen’: Eigenvalue decomposition, can be combined with shrinkage.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt><strong>shrinkage</strong><span class="classifier">string or float, optional</span></dt><dd><dl class="simple">
<dt>Shrinkage parameter, possible values:</dt><dd><ul class="simple">
<li><p>None: no shrinkage (default).</p></li>
<li><p>‘auto’: automatic shrinkage using the Ledoit-Wolf lemma.</p></li>
<li><p>float between 0 and 1: fixed shrinkage parameter.</p></li>
</ul>
</dd>
</dl>
<p>Note that shrinkage works only with ‘lsqr’ and ‘eigen’ solvers.</p>
</dd>
<dt><strong>priors</strong><span class="classifier">array, optional, shape (n_classes,)</span></dt><dd><p>Class priors.</p>
</dd>
<dt><strong>n_components</strong><span class="classifier">int, optional (default=None)</span></dt><dd><p>Number of components (&lt;= min(n_classes - 1, n_features)) for
dimensionality reduction. If None, will be set to
min(n_classes - 1, n_features).</p>
</dd>
<dt><strong>store_covariance</strong><span class="classifier">bool, optional</span></dt><dd><p>Additionally compute class covariance matrix (default False), used
only in ‘svd’ solver.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.17.</span></p>
</div>
</dd>
<dt><strong>tol</strong><span class="classifier">float, optional, (default 1.0e-4)</span></dt><dd><p>Threshold used for rank estimation in SVD solver.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.17.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>coef_</strong><span class="classifier">array, shape (n_features,) or (n_classes, n_features)</span></dt><dd><p>Weight vector(s).</p>
</dd>
<dt><strong>intercept_</strong><span class="classifier">array, shape (n_classes,)</span></dt><dd><p>Intercept term.</p>
</dd>
<dt><strong>covariance_</strong><span class="classifier">array-like, shape (n_features, n_features)</span></dt><dd><p>Covariance matrix (shared by all classes).</p>
</dd>
<dt><strong>explained_variance_ratio_</strong><span class="classifier">array, shape (n_components,)</span></dt><dd><p>Percentage of variance explained by each of the selected components.
If <code class="docutils literal notranslate"><span class="pre">n_components</span></code> is not set then all components are stored and the
sum of explained variances is equal to 1.0. Only available when eigen
or svd solver is used.</p>
</dd>
<dt><strong>means_</strong><span class="classifier">array-like, shape (n_classes, n_features)</span></dt><dd><p>Class means.</p>
</dd>
<dt><strong>priors_</strong><span class="classifier">array-like, shape (n_classes,)</span></dt><dd><p>Class priors (sum to 1).</p>
</dd>
<dt><strong>scalings_</strong><span class="classifier">array-like, shape (rank, n_classes - 1)</span></dt><dd><p>Scaling of the features in the space spanned by the class centroids.</p>
</dd>
<dt><strong>xbar_</strong><span class="classifier">array-like, shape (n_features,)</span></dt><dd><p>Overall mean.</p>
</dd>
<dt><strong>classes_</strong><span class="classifier">array-like, shape (n_classes,)</span></dt><dd><p>Unique class labels.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis</span></code></a></dt><dd><p>Quadratic Discriminant Analysis</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The default solver is ‘svd’. It can perform both classification and
transform, and it does not rely on the calculation of the covariance
matrix. This can be an advantage in situations where the number of features
is large. However, the ‘svd’ solver cannot be used with shrinkage.</p>
<p>The ‘lsqr’ solver is an efficient algorithm that only works for
classification. It supports shrinkage.</p>
<p>The ‘eigen’ solver is based on the optimization of the between class
scatter to within class scatter ratio. It can be used for both
classification and transform, and it supports shrinkage. However, the
‘eigen’ solver needs to compute the covariance matrix, so it might not be
suitable for situations with a high number of features.</p>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LinearDiscriminantAnalysis</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">LinearDiscriminantAnalysis()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="o">-</span><span class="mf">0.8</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]]))</span>
<span class="go">[1]</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decision_function</span></code></a>(self, X)</p></td>
<td><p>Predict confidence scores for samples.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y)</p></td>
<td><p>Fit LinearDiscriminantAnalysis model according to the given</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict class labels for samples in X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_log_proba</span></code></a>(self, X)</p></td>
<td><p>Estimate log probability.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_proba</span></code></a>(self, X)</p></td>
<td><p>Estimate probability.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the mean accuracy on the given test data and labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Project data to maximize class separation.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">solver='svd'</em>, <em class="sig-param">shrinkage=None</em>, <em class="sig-param">priors=None</em>, <em class="sig-param">n_components=None</em>, <em class="sig-param">store_covariance=False</em>, <em class="sig-param">tol=0.0001</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/discriminant_analysis.py#L250"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function">
<code class="sig-name descname">decision_function</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_base.py#L247"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict confidence scores for samples.</p>
<p>The confidence score for a sample is the signed distance of that
sample to the hyperplane.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array_like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>Samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)</dt><dd><p>Confidence scores per (sample, class) combination. In the binary
case, confidence score for self.classes_[1] where &gt;0 means this
class would be predicted.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/discriminant_analysis.py#L408"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit" title="Permalink to this definition">¶</a></dt>
<dd><dl>
<dt>Fit LinearDiscriminantAnalysis model according to the given</dt><dd><p>training data and parameters.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.19: </span><em>store_covariance</em> has been moved to main constructor.</p>
</div>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.19: </span><em>tol</em> has been moved to main constructor.</p>
</div>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Training data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>Target values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_base.py#L279"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class labels for samples in X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array_like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>Samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>C</strong><span class="classifier">array, shape [n_samples]</span></dt><dd><p>Predicted class label per sample.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba">
<code class="sig-name descname">predict_log_proba</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/discriminant_analysis.py#L540"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimate log probability.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>C</strong><span class="classifier">array, shape (n_samples, n_classes)</span></dt><dd><p>Estimated log probabilities.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba">
<code class="sig-name descname">predict_proba</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/discriminant_analysis.py#L518"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimate probability.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>C</strong><span class="classifier">array, shape (n_samples, n_classes)</span></dt><dd><p>Estimated probabilities.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L344"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the mean accuracy on the given test data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/discriminant_analysis.py#L492"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Project data to maximize class separation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">array, shape (n_samples, n_components)</span></dt><dd><p>Transformed data.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-discriminant-analysis-lineardiscriminantanalysis">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis.LinearDiscriminantAnalysis</span></code><a class="headerlink" href="#examples-using-sklearn-discriminant-analysis-lineardiscriminantanalysis" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Shows how shrinkage improves classification. "><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_lda_thumb.png" src="../../_images/sphx_glr_plot_lda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py"><span class="std std-ref">Normal and Shrinkage Linear Discriminant Analysis for classification</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example plots the covariance ellipsoids of each class and decision boundary learned by LDA..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_lda_qda_thumb.png" src="../../_images/sphx_glr_plot_lda_qda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py"><span class="std std-ref">Linear and Quadratic Discriminant Analysis with covariance ellipsoid</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 a..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_pca_vs_lda_thumb.png" src="../../_images/sphx_glr_plot_pca_vs_lda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py"><span class="std std-ref">Comparison of LDA and PCA 2D projection of Iris dataset</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various embeddings on the digits dataset."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_lle_digits_thumb.png" src="../../_images/sphx_glr_plot_lle_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py"><span class="std std-ref">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Neighborhood Components Analysis for dimensionality reduction."><div class="figure align-default" id="id5">
<img alt="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" src="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py"><span class="std std-ref">Dimensionality Reduction with Neighborhood Components Analysis</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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